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STUDY OF THE POSSIBLE LINKS BETWEEN CO2 EMISSIONS AND
EMPLOYMENT STATUS
Ioannis Vardopoulos
Division of Environmental Conservation and Management, Faculty of Pure and Applied
Sciences, Open University of Cyprus.
Department of Home Economics and Ecology, School of Environment, Geography and Applied
Economics, Harokopio University of Athens, Greece.
Zoi I. Konstantinou
Division of Hydraulics and the Environment, Department of Civil Engineering, Aristotle
University of Thessaloniki, Greece.
Abstract While humanity confronts the results of past policies in economic, social and
environmental terms, attempting to mitigate the evident impacts on the environment and human
life, this study attempts to explore the possible relationship between unemployment rates and
their affect on climate change through carbon dioxide (CO2) emissions from human economic
activity. It is certain that issues of this magnitude are deeply interdisciplinary and their
profound understanding requires complex and multi layered approach, in conjunction with the
ensuing impacts in all different aspects. In this study, the formulation of a correlation between
those two variables is attempted, through statistical processing of international data. Results
are indicating towards a moderate correlation to an extent sufficient enough to allow research
on the causes. The objections attribution is based on sample survey, using predefined
assumptions. The aggregated results of the survey, acknowledging the delimitations set for the
scope of this study, did not provide an unequivocal correlation on cause basis, but revealed all
the parameters needed to be included in a future statistical analysis and survey in order for
robust conclusions to be reached.
Key Words: Climate Change, CO2 Emissions, Environmental Footprint, Unemployment
Acknowledgments: The study was initially presented at the 1st International Workshop on
Economic Growth and Sustainable Development on 9-10 September in Athens, Greece. We
thank the workshop participants and anonymous referees for helpful comments and
suggestions.
Introduction
What is ultimately more important for a modern society, Climate Change or
unemployment (ILO, 1982)? This is an important dilemma created as a result of the proposed
shutdown of a large steel plant. Such industries release large amounts of CO2, but are also the
main foundation for several modern economies (Worldsteel, 2014). A similar situation applies
to coal. Burning coal releases large amount of CO2 (Pacyna et al., 2016), but for the extraction
hundreds of thousands people are employed (Chung, 2016). Whereas coal is not extinct, but
instead inactive in the ground, formed millions of years ago (Taylor et al., 2009), it would be
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theoretically possible for the coal-mining industry to employ hundreds of thousands of workers
again sometime in the near future. Thus, those advocating against unemployment will naturally
want to open the mines again, but those supporting awareness against climate change and
global warming will seek to keep carbon in the ground forever.
Both the major issues of Climate Change (Zhang et al., 2011; IPCC, 2014) and
unemployment (Rontos et al., 2016) cause global concern (IPCC, 2014) under the modern,
fragile, socio-economic regime of completely free market (Bockman, 2011). The correlation
between the two under the current, globally evolving, social and political conditions may
constitute keynote knowledge for understanding and mitigating both. However, the theoretical
mechanism that connects those two social and economic variables, for the scope of this
research, lies within the context of Cato’s diagram indicating the relationship between the three
pillars of sustainability (Scott-Cato, 2008).
Figure 1: The 3 pillars of sustainability (image from UTSC Sustainability Office)
In this context, this research approaches Climate Change through the probable causes of
human economic activity, which increases greenhouse gas concentrations in the atmosphere
(Janssens-Maenhout et al., 2011), in relation to unemployment rates and ultimately, the extent
on which the latter affects or not CO2 emissions. While the relation between economy and
climate change has been thoroughly studied (Pie et al 2013, 2014) and the effect of
unemployment and inflation in an economic system have been also analytically investigated
(Alogoskoufis, 2016) (see Okun’s Law, Philips Curve, Kuznets Curve Keynesian economics),
the possible correlation between unemployment and Climate Change (through CO2 emissions)
remains unidentified.
Within the context of efforts in finding a joint solution, if Climate Change and
unemployment proved to be mutually exclusive then how should these two issues be
addressed? If employment is found to increase CO2 emissions per capita, then would it make
sense to promote, as a measure of mitigating Climate Change, to deliberate exclusion -under
welfare state insurance benefits- of a percentage (say 30%) of the individuals currently in the
labour force; i.e. be paid not to produce? Or, if this question is posed differently, is it possible
that the extent of the retirement age has a specific environmental impact and, thus, may have
an indirect cost that has not been taken under account yet?
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Accordingly, parallel to the current market ethics (Weber and Parsons, 2013) on which
most modern western societies are built on, should there be incentives for job creation even if
it leads to increased CO2 emissions?
The so-called green economy and/or green job, is for many researchers a myth, or better
an illusion, for many and different reasons, but mainly because it employs a small number of
people whose wages are overwhelmingly subsidized by government funds. Thus, most green
industries are simply unprofitable or not economically viable without those funds. Funds,
though, resulting from the state revenues, ie. taxes. (indicatively Morriss et al., 2009; Musu,
2010; Mulvaney, 2014). The current European policy and strategy in support of the so called
green jobs and renewably energy sources, dates back at least a decade and has become at some
point a primary proposal from the USA. However, considering the case in Europe revealed that
these policies are terribly damaging and economically unproductive (Green, 2011). Hence,
following these studies, one can conclude that the green job agenda actually contributes in
losing jobs, regarding the jobs lost per green job created and the environmental impact occurred
with each MW of renewable energy installed. Therefore, is there in the current economic
situation in Europe, an ally for employment besides green jobs?
Methodology
The selected methodological approach for this research had two major steps. A
quantitative comparative statistical analysis was applied to CO2 emissions data1 and
unemployment rates data2 of 38 countries around globe, for a time period between 1980 and
2014. Countries selection was made upon listing the top 20 countries in the world in CO2
emissions, the top 20 countries in the world in unemployment rates as appeared from data1-2
extracted during this research, and the countries which were included in the survey research.
Statistical data analysis included the calculation of the average, mode, media, mean and
standard deviation, as well as the calculation of the correlation coefficient between the
variables of each country throughout the whole time period, and the development of scatter-
grams and trend-lines for the data-sets. The aforementioned correlation was used to develop
the initial hypothesis, which has given the delimitations set for the scope of the current research,
that CO2 emissions correlate with unemployment.
In order to investigate further if there is a causal relationship between unemployment and
CO2 emissions, and hence with climate change, an online survey was designed and conducted
in March 2016 with a single month duration. In accordance with the guidelines of this research,
duration was subject of the response rates and the sufficiently large data acquired for analysis.
1 Global per capita CO2 emissions from human economic activity, 1980-2014. Emissions: 2015 update with 2014 emissions of fossil fuel use and industrial process emissions (cement production, carbonate use of limestone and dolomite, non-energy use of fuels and other combustion). Substance: CO2 country totals excluding short-cycle biomass burning and excluding large-scale biomass burning. Unit: ton (Mg) CO2 per capita and per year. Date: 25/11/2015. Sources: EDGARv4.3, EC, JRC, Netherlands Environmental Assessment Agency. The EDGARv4.3FT2014 emissions are calculated based on the energy balance statistics of IEA (2014), BP (2013-2014) data of BP Statistical Review of World Energy, June 2015, Chinese coal consumption data of the China Statistical Abstract, October 2015, UNDR (2013) World Population Prospects, the 2012 Revision Report United Nations, Department of Economic and Social Affairs, Population Division. Reference: Olivier, J.G.J., Janssens-Maenhout, G., Muntean, M. and Peters, J.A.H.W. (2015) Trends in global CO2 emissions: 2015 Report. PBL Netherlands Environmental Assessment Agency, The Hague; European Commission, Joint Research Centre (JRC), Institute for Environment and Sustainability (IES). JRC98184, PBL1803, Internet: http://edgar.jrc.ec.europa.eu/news_docs/jrc-2015-trends-in-global-co2-emissions-2015-report-98184.pdf, November 2015 2 Global Unemployment Rates, 1980-2014. Units: Percent of total labor force. Source International Monetary Fund, World Economic Outlook Database, April 2015, CIA World Factbook.
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The questionnaire was drawn up to record the environmental footprint of those who are
working/employed in relation to those unemployed. As certain aspects were beyond the scope
of the current research, the latter were not separated in respect to types of unemployment
(Skenderi and Uka, 2015). Delimitations were also set in regard to geographic, economic and
social data. For example, there was no distinction between those leaving in a city centre and
those leaving in suburbs regarding their needs for public transportation use, or between those
leaving in different geographic latitudes regarding their needs for electricity (i.e.
heating/cooling).
The developed survey was mainly based on closed-ended questions, with the exception
of some questions regarding demographics. The main type of questions included in the survey
were factual ones, i.e. respondents were invited to answer how they think, feel and express an
opinion or intention regarding specific things that concern them, that are familiar with or that
can easily estimate, eliciting knowledge and attitudes in relation to climate change,
environmental values, behaviours and demographics.
The survey consisted of two essential parts: a) one focused in questions through which
to determine the environmental footprint and b) one to identify the demographic profile of the
survey responders. The environmental footprint determination questions were developed in
accordance with the Global Footprint Network measuring the supply of and demand on nature
(Global Footprint Network, 2016). The order in which questions became available, took under
account the levels of comfort of the responder, as well as attraction factors. The first part
introduces the respondent directly to the scope of the research with questions both general and
specific content, i.e. Do you believe global warming is caused by humans?; Do you consider
“thinking green” an economic luxury?; What is the level of carbon dioxide increased by?;
What is the unemployment rate today?; Are you currently employed?; The second part basically
separates the respondents to employed and unemployed. For the employed respondents,
clarification questions regarding their working environment follow, while for the unemployed
respondents, the questions try to determine their type of unemployment. Although, as
previously mentioned, the type of unemployment variable was beyond the scope of the current
research, it was deemed necessary to be recorded for statistical accuracy and probable future
use. The third section is focusing on examining the environmental footprint of each respondent,
i.e. How often do you eat meat, fish, diary?; How much do you spend on clothing and household
equipment?; What is the size of your house?; Do you recycle and use energy saving lighting?;
How far do you travel by car, public transportation, boat and airplane?; The last part was
dedicated to examine certain demographic characteristics of the sample, i.e. the gender, the
age, the education status, the income, the political affiliations (McCright, 2010), the relevance
to environmental science.
The questionnaire was send via e-mail to a random sample of people all around the globe
in about 4,500 e-mail addresses from OUC’s e-mail data base for surveys and was shared in
social media networks with estimated engagement of about 5,250 users (N=510; response
rate=5,23%). In some participating countries the response rate is moderate to low.
The questionnaire was developed in a neutral manner, without leading and influencing
the respondents, hence demographic questions were placed in the last section, aiming in
excluding any possible emotional responses (Watson and Greer, 1983). Upon completion of
the survey, processing and statistical analysis of the data followed. A first analysis separated
the complete questionnaires and excluded those who did not meet certain criteria3. Thus from
the total 510 initial questionnaire replies, 110 were excluded meaning that 78.4% of the sample
answered was utilise for this research.
3 Criteria included the exclusion of those aged less than 16 years old, of those not stating their age, of those not
stating their country of residence, and of those that did not answered in all the questions of the survey.
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For this study and further to the initial analysis, a separation of the responders was applied
regarding their country of residence in order to deeper investigate and analyze the demographic
profile characteristics in relation to some specific country characteristics and the environmental
footprint in an attempt to additionally recognize potential patterns with regard to the initial
hypothesis. The two countries that were selected for additional study were the second and third
by rate of responds, thus Cyprus and United Kingdom. Greece, the first country by rate of
responds, was not selected due to the presence of certain characteristics in the under research
time of period of the political and financial state of the country that could affect the final
outcome (ex. supernumery elections, extreme variations of the unemployment rates, prolonged
economic crisis with effects in society, etc.). Moreover, it is an attempt to recognize if the high
respond rates from Greece have or not misleadingly shaped the final outcome.
Results
The comparative statistical analysis from the data regarding the CO2 emissions and
unemployment rates are presented in Table1. Furthermore, a scatter-gram with the trend-line
of all the above mentioned values of the data is presented in Figure1.
The analysis between the variables, prove correlation. In the future, a more empirical
evaluation, based on regression analysis, can be implemented in order to derive more confident
conclusions. The correlation consists of a general tendency, but there are also different
indications on particular countries. Those findings come to link directly CO2 emissions and
unemployment, in fact, relate each one’s economic activity to climate change through his
environmental footprint. However, despite the significance of those results, which help to
identify potential causes, it is not possible to determine a causal relationship between the two
variables; correlation does not imply causality. Therefore the survey research was necessary.
Table 1: Correlation coefficient in absolute terms of per capita CO2 emissions values1 and
of unemployment rates2 per country. Statistical indicators.
Country R Country R Average
Australia 0.689 Japan 0.659 0.392
Austria 0.627 Mexico 0.152 Mode
Bahrain 0.843 Netherlands 0.368 0.610
Belgium 0.174 New Zealand 0.060 Median
Brazil 0.276 Norway 0.005 0.366
Bulgaria 0.378 Pakistan 0.607 Mean Deviation
Canada 0.512 Poland 0.303 0.227
China 0.679 Portugal 0.253 St. Deviation
Cyprus 0.105 Russian Federation 0.057 0.269
Czech Republic 0.234 Slovakia 0.001
Denmark 0.011 South Africa 0.316
France 0.607 Spain 0.741
Germany 0.260 Sweden 0.412
Greece 0.038 Switzerland 0.383
Hungary 0.904 Taiwan 0.793
Iceland 0.598 Turkey 0.518
Indonesia 0.827 Ukraine 0.264
Iran 0.019 United Kingdom 0.364
Italy 0.281 United States 0.570
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In order to develop a reliable measure of the environmental footprint of the two social
groups under study, the statistical analysis of the survey responds, discussed below, focuses on
three sets of variables, i.e. quantitative demographics; qualitative demographics and lifestyle
measures including education, knowledge and political orientation; quantitative environmental
behaviour of the sample.
Figure 2: Scatter gram of all values1-2 of per capita CO2 emissions and of unemployment
rates of all countries included in this research of a time period of 1980-2014.
In order to develop a reliable measure of the environmental footprint of the two social
groups under study, the statistical analysis of the survey responds, discussed below, focuses on
three sets of variables, i.e. quantitative demographics; qualitative demographics and lifestyle
measures including education, knowledge and political orientation; quantitative environmental
behaviour of the sample.
Table 2: Demographic profile of survey respondents for the total sample and for Cypriot
and UK respondents.
Total Cyprus UK
Gender
Female 60.8% 63% 76%
Male 39.2% 38% 24%
Age
18-30 33% 28% 57%
30-45 52% 64% 33%
45-60 13% 6% 10%
>60 2% 3% 0%
Education
Basic Education 0.6% 0% 0%
Secondary Education 4.3% 4% 0%
Higher Education 39.3% 29% 19%
Master of PhD Education 55.8% 67% 81%
Relation to Environmental Science
No 77.8% 75% 86%
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Yes 22.3% 25% 14%
Political Affiliation
Centre 27.5% 25% 24%
Left 26% 13% 33%
Extreme Left 2.3% 0% 0%
Right 12.8% 11% 0%
Extreme Right 0.2% 0% 0%
Don’t know/No opinion 31.2% 51% 43%
Monthly Income
0-500€ 22.8% 13% 24%
501-1000€ 26.1% 22% 0%
1001-1500€ 23.3% 26% 14%
1501-2000€ 11% 22% 10%
2001-2500€ 6% 8% 14%
2501-3000€ 3.2% 6% 14%
3001-3500€ 2.8% 3% 10%
3501-4000€ 1.4% 0% 5%
4001-4500€ 0.4% 0% 0%
4501-5000€ 0.4% 0% 0%
>5000€ 2,6€ 0% 10%
Currently Employed
Yes 77% 81% 76%
No 23% 19% 24%
Survey’s included demographic measures are presented in Table2 (qualitative and
quantitative). Table3 presents a synthesis of the environmental footprint of those responders
unemployed and those that are currently working in terms of the highest percent of the given
responds.
Table 3: Environmental footprint comparison table of those currently employed and those
currently unemployed, as recorded from the survey’s highest percent responds for each one of
the two groups of people for the total sample along with Cypriot and UK respondents.
Total Cyprus UK
Currently employed? No Yes No Yes No Yes
How often do you eat meat? Occasionally 48% 57% 57% 57% 80% 56%
How often do you eat fish? Infrequently 61% 68% 64% 64% 100% 44%
How often do you eat eggs,
milk and dairy?
Often 43% 43% 29% 41% 40% 50%
What clothing and footwear
goods do you buy each
month?
Not at all, unless
it is an absolute
necessity
48% 50% 50% 41% 80% 38%
How much do you spend
per year on household
furnishings and appliances?
Very little
(around 200€) 74% 0% 71% 41% 60%
69%
Not very much
(around 600€)
17% 63%
How much of your paper,
cardboard and plastic waste
do you recycle?
All possible 30% 38% 21% 33% 80% 44%
Most 43% 26%
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How many people live in
your household?
Two 26% 29% 7% 24% 20% 56%
Three 36% 10% 40% 6%
Four 36% 41%
What is the size of your
house?
Small (around
100m2)
43% 38% 60% 31%
Medium (around
150m2)
43% 34%
Do you use energy saving
lighting?
All the lights in
my house are
energy efficient
30% 45% 43% 48%
Half of the lights
in my house are
energy efficient
40% 44%
I do not know 60% 6%
What do you typically
spend per month on
electricity for your home?
Around 60€ 43% 54% 36% 52% 80% 56%
Around 120€ 43% 40%
How far do you travel by
car each week (as a driver
of passenger)?
Around 80km 39% 41% 57% 40%
I never ride a car 100% 63%
What is the fuel
consumption of the car you
travel in most often?
Medium
(8L/100km)
57% 55% 79% 67% 60% 31%
Very Low
(4L/100km)
40% 63%
How often do you drive in a
car with someone else?
Occasionally 30% 36% 36% 29% 40% 50%
Often 21% 34%
How far do you travel by
public transit each week
(bus, rail, subway, etc)?
1 to 10 km 26% 21%
0 km 22% 43% 79% 97%
11 to 40 km 80% 31%
How many hours do you fly
each year?
3 hours trip(s) 26% 20% 21% 29%
I do not 22% 22% 36% 19%
30 hours trips 40% 19%
15 hours trips 20% 38%
How many hours do you
travel by boat each year?
I do not 43% 43% 71% 88% 100% 38%
Discussion
Given that Climate Change does not concern in an equal, horizontal manner all parties
(Parry et al, 2007), vulnerable points of societies and economies must be studied among
countries through the anthropogenic social, psychological, economic and cultural background,
with its resulting biophysical alterations and dynamics, regardless of their geography location.
How do economic losses from unemployment affect greenhouse gas emissions in relation
to losing jobs in core labour industries, accumulating non-productive population mainly in the
city centres and how does this reflect in attitudes, perceptions and behaviours? This article
offers a view on the insufficient opportunities to work (unemployment) -as a major issue and
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challenge facing both developed and developing countries (Ashford et al., 2012)- and the
probable caused environmental effects, in order to arrive to an informed judgment concerning
whether, or more appropriately under what conditions, a deliberate boost in job opportunities
makes sense for a region in crisis. Our stating point is not a focus on reducing consumption,
but on emphasizing that social and environmental sustainability require sustainable earning
capacity for poor and middle-class people.
Global Warming affects both local and regional market’s risks and opportunities
worldwide (Dell et al., 2009, 2012, 2014). The main impact occurs, as expected, in industries
depending on climate (eg. agriculture, fisheries, forestry, tourism, insurance, health care, etc.).
Climate Change affects global employment and economic instability, extending far beyond the
industries’ most sensitive to climate (Field, 2014). Climate Change is absolute and every aspect
of human life is affected to some extent by the patterns and the changes (Baldwin, 2014).
Therefore, as the effects of Climate Change accumulate, people will be obliged in changes to
their lifestyle. In specific parts of the world, the changes to occur will be essential and extensive
(Clark et al., 2016) opening up new investment values (Webster et al., 2003) and realignments
in employment. In other parts of the world, those changes may be of less importance, but even
then, if the will occur within a short time of period (Clark et al., 2016) may become an
aggregating force for rearrangement in business opportunities.
The optimum interpretation of unemployment, as expressed, is an environmental tool to
break the constantly accelerating GDP growth as to maintain the sustainability levels in the
context of resource consumption and environmental impacts (McVittie et al, 2008). Full
employment condition, aiming at developing more environmentally efficient production and
consumption patterns, could potentially provide an important and constantly cumulative
environmental benefit, combined with reduced consumption of resources (Friedman, 2008).
Maybe so, the future of the economy and manpower will benefit from the resultant structural
increases in living standards and GDP growth.
In this study, the survey was not targeted to a sample of the total population and was
conducted as previously mentioned via e-mail written in English. Hence, the high
representation of Greek and Cypriot responders (i.e. 77%) along with the high percentage (i.e.
95%) in high educational qualifications –(see foreign language speakers) aligned with the
wider literature findings stating that educated people are more likely to complete a
questionnaire (Curtin et al., 2000; Singer et al., 2000)- are partly justified. Although the study
is limited, it is important to note that is one of the first studies on the subject. From that
perspective and in parallel with the scope of this research the level of representativeness was
considered enough. Indeed though, for conclusive results perhaps the survey should have been
left available longer time of period and should be addressed in one or two more language
options.
Consistent with the wider literature (indicatively Lorenzoni and Pidgeon, 2006;
McCright and Dunlap, 2011; Nisbet, 2009; Weber and Stern, 2011), findings from this study
suggest widespread knowledge and concern about climate change. However, this, does not
translate into personal engagement namely in terms of cognition, affect and behaviour
(Lorenzoni, Nicholson-Cole and Whitmarsh, 2007).
Indeed, many of the participants/responders do not consider “thinking green” an
economic luxury (61.5%), believe global warming is caused by humans (89.5%), see a
relationship between unemployment rates and climate change (46.5%), even though their field
of studies or occupation is not related to environmental science (77.8%), recycle (90.8%), use
energy saving lighting (86.5%), use cars with fuel consumption below 10L/100Km (84.3%),
and consider as a top priority growing a sustainable economy (52.5%).
Accordingly, for Cyprus, it is clear for those currently unemployed that are not prevailing
a raising awareness, but rather are confused.16% of the responders consider “thinking green”
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an economic luxury, just 22% believe that global warming is caused by humans, they are not
aware of the level of carbon dioxide increase (since the industrial revolution), but do consider
environmental protection (global warming mitigation) and growing a sustainably economy top
priorities, compared to decreasing unemployment rates. According to the same sample
responders are not aware of the current unemployment rates, while they don’t identify a relation
between employment status and climate change.
In parallel UK citizens responders that are currently unemployed do consider “thinking
green” an economic luxury by 60% and they strongly believe that global warming is cause by
humans (100%). Similarly to the Cypriot responders they are unaware of the level of carbon
dioxide increase (80%) but they consider decreasing unemployment rates (60%) as a top
priority even if they are not aware of the range of these rates, in conjunction with those currently
working, who consider top priority growing a sustainable economy (75%). UK responders also
do not identify a relationship between employment status rates and climate change (40%),
unlike the currently employed (50%).
As the goal of this article is not to conduct a cross-country comparison of the effect of
unemployment in climate change, but to examine this relation independently from country
characteristics, to further indicate the absence or presence of some categorical effects that may
be expected to shift the outcome (ex. country characteristics, occurrence of wars, major strikes,
financial crisis, etc) the use of dummy variables should be an appropriate approach for further
analysis.
Observations of the aforementioned findings indicates that the environmental footprint
of the two community groups under study is common. Furthermore, the examination of the two
separate countries showed none significant alternations in consumer habits and life style
choices, hence in the total environmental footprint. This research’s result though, can be
challenged taking into account the delimitations set for the scope of this research. The fact that
from this research do not arise a clear causal relationship to the initial hypothesis, does not
imply it does not exist, especially if in the future the research emphasises in the comparison of
developing countries as well. Besides, the most optimistic interpretation of Kuznets
environmental curve states, growth in income is itself the solution to environmental problems
(Andreoni and Levinson, 1998).
In the current literature and the scientific field of environmental science, appears to be
none research on the questions raised in the current study.
The political, economic, social and even biological correlation and interpretation of all
indicators that arise, such as per capita GDP, per capita CO2 emissions, unemployment rate,
long-term unemployment index, education index, geographical features, political conditions,
are subject to further investigation, evaluation and correlation. Given the combination of all
parameters from a new methodological approach and statistical analysis, it will become
possible to address conclusively this multidimensional theorem. Further research on this
subject should include, besides the obvious gradual removing of the delimitations set for the
scope of the current paper, a cross-country comparison, the use of an econometric technique
based on probabilistic models and a more mathematical approach (empirical curve equation).
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